Business Analytics vs Business Intelligence

Business Analytics vs Business IntelligenceBusiness analytics and business intelligence, to most people, seem to be the same thing. The two terms are related, but they are by no means identical.

Using intelligence and analytics is essential to succeeding in a competitive market. In order to do that, you have to understand the terms.

So, what’s the difference between business analytics vs business intelligence?

Business Analytics vs Business Intelligence

The confusion between the two terms comes mostly from the fact that they work together. Business analytics feed into business intelligence and vice versa. They’re both off-branches of data science.

We’ll start with business analytics.

What is Business Analytics?

The term describes all methods used by businesses in order to engage with a company or market’s data. This data is essential for a business to understand where it lies in the grander scheme of the market. It informs a business how it should proceed in the most successful way.

Further, companies use their data and the data of other companies to identify issues and their solutions. Say, for example, that Company A is having a difficult time with its marketing platform. It wants to strengthen the correlation between marketing dollars spent and sales.

Company A could simply look at Company B’s successful marketing platform and grovel in shame and disappointment with themselves. On the other hand, Company A could run some analytics on the data they have about Company B.

Doing so would allow them to find out why Company B is so successful at the moment. They should also run analytics on their own data and identify the problem.

Analytics run deep into the heart of modern business. Statistical analysis leads to accurate insights into what should happen in the future and why things happened in the past.

How it Works

The bread and butter of business analytics is statistical analysis. Statistical analysis requires gathering data samples from every relevant region and pouring over those samples to find patterns.

The goals of statistical analysis are pretty straightforward and methodical. First, the analyst needs to describe the nature of the data in front of them. They must then identify where that data sample fits into the grander scheme of the market.

In other words, is the data sample representative of the entire population of data? They must then create a model of that data to reflect the insights gained from their analysis. This could mean making a graph, writing a paper, or doing something else that expresses the new understanding.

The insights from the data sample must then be backed up by evidence and support. If the analysis bred a successful insight into the market or business practices of a company, that insight will be used to inform future decisions.

Business analytics is ultimately an attempt to find trends in the flow of business and use those trends to the business’ advantage.

Business Intelligence

Business intelligence is kind of like the business analytics’ big brother. It operates in a similar way but approaches business from a wider scope. Business intelligence systems aim to cut the fat from a business’ operations.

Business intelligence helps to organize operations and support or inform the decision-making that is made by business analysts. Business analytics seeks to find insights that might prompt a business to make changes, while business intelligence keeps an eye on those changes to make sure they work.

You can think of business intelligence like a parent, keeping an eye on its unruly, yet creative child named business analytics. Some of the things that the child does will be extremely effective, intelligent, and radical. At the same time, a child’s decisions can’t be expected to be right on all of the time.

You may still be thinking that there’s no difference between business analytics and business information. The difference might be easier to understand if we focus on two terms: reporting and analytics.

Reporting vs Analytics

Business information is essentially reporting on the current state of a business’ operations overall. Reporting is the act of gathering data into relevant, summarized packages and using it to get a decent look at how well a company is doing in different areas.

So, reporting sort of makes sense of a company’s data and turns it into something that can be used to guide decision-making and reflect on the state of affairs. Reporting should be thought of as a sort of presentation of the company’s data.

Analytics serve as a tool to look at the data under a microscope. Analytics tease out why data is relevant and what it means, while reporting takes that data after it’s understood and applies it to the general scope of the business.

Business Intelligence Technology

There’s a constant need for improved business intelligence technology because businesses are always competing to get the upper hand on one another. This has lead to a great sophistication in the technology that’s used for business information and analytics.

Modern tools process relevant data and require very little human input to give readings and predictions. A person is needed to interpret the data an implement the solutions in real life, but software now has the capability of gathering, analyzing, and presenting data in simple formats.

The method of presenting data in a manageable format is called data visualization. Data visualization makes the information accessible to far more people than reports and complex graphs would be.

Technologies used today also have the ability to synthesize with the tools and operations of specific businesses. That means being able to take information in from the tools and operations that are currently in place. They also have the ability to pull data from online sources.

Sound Interesting?

The difference between business analytics vs business intelligence is slight, yet important. If you’ve got an eye for details that are slight, yet important, you might be interested in a data science career.

Analyzing data, making predictions, and gathering insights are all in a day’s work for the data scientist. If that sounds interesting to you, we have the information you need.

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